Clarifyd
Services / Training

Training that improves judgement — not just skills.

We help people reason with data confidently: asking better questions, spotting pitfalls, and applying the right methods in the real world.

Data literacy
SQL foundations
Applied data science
MLOps awareness
Discuss a programme → See formats
Outcomes

What changes after the training

  • Teams ask clearer questions and define “success” better
  • People interpret charts and metrics more reliably
  • Analysts write cleaner queries and validate results
  • Stakeholders understand model limitations and risk
Enterprise-aware by design

Built for real constraints

We design training for mixed skill levels, limited time, and the tools people actually use. Sessions are hands-on, paced, and focused on transfer into day-to-day work.

  • Practical exercises (not toy problems)
  • Clear takeaways and reusable templates
  • Optional diagnostics focused on understanding

Training pathways

Two tracks, tailored to how different audiences use data. Pick the one that fits — or combine both.

For leaders & decision-makers

Data literacy for better decisions

Build confidence interpreting metrics, questioning assumptions, and using data responsibly — without turning everyone into an analyst.

  • Metrics, uncertainty, and what “good” looks like
  • Common pitfalls: bias, leakage, spurious patterns
  • How to evaluate analyses and ML claims
  • Practical ways to set up teams for success
Workshop: 2–4h Workshop: 1 day Leadership series
For developers & analysts

Build, validate, and ship with confidence

Hands-on training focused on real delivery habits: querying, validation, modelling, and production awareness.

  • SQL foundations + validation patterns
  • Feature building, baselines, and evaluation
  • Reproducibility, testing, and monitoring mindset
  • MLOps lifecycle awareness for maintainable systems
Cohort: 2–6 weeks Bootcamp: 3–5 days Role-specific
Tailored

Built to your context

We can tailor both tracks to your tools, data, and constraints — from non-technical literacy to applied data science delivery.

  • Tooling-specific (e.g. your SQL environment)
  • Domain-specific scenarios and s
  • Reusable internal materials and templates
Topics (s)

What we cover

Data literacy & reasoning

How to think about data quality, bias, metrics, and uncertainty.

SQL & working with data

Query patterns, validation habits, joins, aggregation, and performance basics.

Applied modelling

From baselines to evaluation: understanding what models can and can’t do.

MLOps awareness

Lifecycle thinking: deployment risk, monitoring, drift, and maintainability.

We tailor depth to the audience — from non-technical literacy to applied data science.

Representative programme

graduate cohort (2 weeks)

A structured programme for early-career analysts entering a regulated, production-oriented environment.

Week 1 — Working with data
Foundations
SQL foundations, data validation habits, and practical analytics patterns.
Week 2 — ML in production
Applied
Model lifecycle, deployment considerations, monitoring, and responsible iteration.
How we measure impact

Short diagnostics, practical exercises, and observable improvements in how people reason, query, and validate.

FAQ

Do you train non-technical audiences?
Yes — literacy sessions are designed for decision-makers and cross-functional teams. The aim is confidence, interpretation, and better questions.
Can you tailor content to our tools and datasets?
Yes. Tailored programmes can align to your stack (e.g. SQL environment, notebook tooling) and your domain scenarios.
Is this classroom-style or hands-on?
Hands-on by default. We combine short explanations with exercises and guided reflection so people understand the “why”, not just the steps.
Next step

Tell us who the training is for.

Share the audience, current level, tools, and timeline — we’ll suggest a format and a sensible syllabus outline.

Email us →